This work presents an innovative Doppler frequency estimation technique, particularly suited for GNSS receivers operating in vehicular scenarios. Mass-market and commercial navigation devices are more and more exploited for in-car navigation and for vehicular applications based on positioning. However, the low computational burden affordable by such devices requires the implementation of low complexity algorithms, allowing real-time and on-demand processing. This is the case for instance of open-loop architectures and of MLE-based techniques, which estimate the frequency component of the GNSS signal through a discrete Fourier transform. A state-of-the-art of such methods is first carried out, outlining their benefits, regarding robustness and stability, and their limitations, mainly concerning the accuracy. Successively an innovative refinement technique is introduced, based on the computation of a frequency correction term. Further enhancements are then proposed to solve particular issues, as the estimation of the sign of the correction term and the impact of the initial frequency error. In particular, zero-forcing and a double FFT – which represent the main contribution of this work – are proposed to increase the accuracy without increasing the computational load. A complete analytical derivation and theoretical description is provided, along with a detailed performance assessment. Finally a performance comparison with existing techniques and with the Cramer-Rao lower bound for frequency estimation is given, confirming the excellent behavior of the proposed algorithm for the signal conditions and strengths typical of a vehicular scenario and in the presence of frequent interruptions.

This work presents an innovative Doppler frequency estimation technique, particularly suited for GNSS receivers operating in vehicular scenarios. Mass-market and commercial navigation devices are more and more exploited for in-car navigation and for vehicular applications based on positioning. However, the low computational burden affordable by such devices requires the implementation of low complexity algorithms, allowing real-time and on-demand processing. This is the case for instance of open-loop architectures and of MLE-based techniques, which estimate the frequency component of the GNSS signal through a discrete Fourier transform. A state-of-the-art of such methods is first carried out, outlining their benefits, regarding robustness and stability, and their limitations, mainly concerning the accuracy. Successively an innovative refinement technique is introduced, based on the computation of a frequency correction term. Further enhancements are then proposed to solve particular issues, as the estimation of the sign of the correction term and the impact of the initial frequency error. In particular, zero-forcing and a double FFT – which represent the main contribution of this work – are proposed to increase the accuracy without increasing the computational load. A complete analytical derivation and theoretical description is provided, along with a detailed performance assessment. Finally a performance comparison with existing techniques and with the Cramer-Rao lower bound for frequency estimation is given, confirming the excellent behavior of the proposed algorithm for the signal conditions and strengths typical of a vehicular scenario and in the presence of frequent interruptions.